University students' generative AI literacy is significantly influenced by country of study, prior AI training, gender, and subject discipline, highlighting the need for tailored educational approaches to develop these critical skills.
Objective: The main goal of this study was to explore university students' perceptions of generative AI (GenAI) literacy in the UK and Hong Kong contexts and to identify the demographic factors that affect their GenAI literacy development.
Methods: The researchers conducted a quantitative study with 234 university students (191 from the UK and 30 from Hong Kong) using an online survey platform (Qualtrics). The survey collected data on six demographic characteristics (country/region, gender, education level, age, subject discipline, and prior AI training) and assessed students' GenAI literacy across four dimensions based on Ng et al.'s (2021) AI literacy framework: knowledge and understanding of AI, use and application of AI, evaluation and creation of AI, and AI ethics. Data were analyzed using descriptive statistics and the Kruskal-Wallis test to determine significant differences between demographic groups.
Key Findings:
- Country/region significantly influenced all four dimensions of GenAI literacy, with Hong Kong students demonstrating more advanced knowledge, usage, evaluation, and ethical awareness than UK students.
- Prior AI training emerged as a significant factor, with students who had taken AI courses showing greater knowledge, more frequent usage, better evaluation skills, and stronger ethical awareness.
- Gender differences were notable, with male students reporting higher confidence in using AI tools (72% vs. 50% of females), more frequent daily usage (88.9% vs. 11.1%), and greater intention to use AI in the future (52% vs. 31%).
- Subject discipline affected knowledge and usage of AI, with computing and technology students showing higher proficiency, though surprisingly, it did not significantly impact ethical considerations.
- Age and educational level did not significantly influence GenAI literacy across the four dimensions.
- Students generally showed limited awareness of AI ethics, suggesting a critical gap in current AI education.
Implications: The study proposes a revised GenAI literacy framework that integrates AI ethics as an essential component across all dimensions (knowledge, application, and evaluation) and acknowledges the influence of macro (government/country), meso (institutional), and micro (individual) factors on GenAI literacy development. This framework can guide universities in designing more effective and inclusive AI literacy programs that address gender disparities, subject discipline differences, and ethical considerations. The findings underscore the urgent need for universities to develop tailored training programs that prepare students to use GenAI ethically and effectively in their academic and professional lives.
Limitations: The study acknowledges several limitations, including a small sample size from Hong Kong (30 students compared to 191 from the UK), potential sampling bias from using convenience sampling within the authors' academic networks, and limitations in the questionnaire design that precluded exploratory and confirmatory factor analysis. Additionally, the study collected data only from two country contexts, which may not represent the full range of global perspectives on GenAI literacy.
Future Directions: The researchers suggest future studies should compare GenAI literacy levels among different universities, explore the impact of micro-level factors in more detail, and investigate learning outcomes for different student groups in AI literacy programs. They also recommend assessing the effectiveness of learning programs designed using their proposed framework and expanding the research to include more diverse country contexts.
Title and Authors: "Factors affecting university students' generative AI literacy: Evidence and evaluation in the UK and Hong Kong contexts" by Xianghan O'Dea, Davy Tsz Kit Ng, Mike O'Dea, and Viacheslav Shkuratskyy.
Published On: 2024
Published By: Policy Futures in Education